Freddie Bickford Smith

According to our database, Freddie Bickford Smith authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Loss-Driven Bayesian Active Learning.

Zhuoyue Huang, Freddie Bickford Smith, Tom Rainforth

CoRR, April, 2026

2025

BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design.

Deepro Choudhury, Sinead Williamson, Adam Golinski, Ning Miao, Freddie Bickford Smith, Michael Kirchhof, Yizhe Zhang, Tom Rainforth

CoRR, August, 2025

Prediction-Oriented Subsampling from Data Streams.

Benedetta Lavinia Mussati, Freddie Bickford Smith, Tom Rainforth, Stephen Roberts

CoRR, August, 2025

Scaling Up Active Testing to Large Language Models.

Gabrielle Berrada, Jannik Kossen, Freddie Bickford Smith, Muhammed Razzak, Yarin Gal, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Rethinking Aleatoric and Epistemic Uncertainty.

Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Making Better Use of Unlabelled Data in Bayesian Active Learning.

Freddie Bickford Smith, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Modern Bayesian Experimental Design.

Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith

CoRR, 2023

Prediction-Oriented Bayesian Active Learning.

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Continual Learning via Sequential Function-Space Variational Inference.

Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

2021

Understanding top-down attention using task-oriented ablation design.

Freddie Bickford Smith, Brett D. Roads, Xiaoliang Luo, Bradley C. Love

CoRR, 2021

2020

The perceptual boost of visual attention is task-dependent in naturalistic settings.

Freddie Bickford Smith, Xiaoliang Luo, Brett D. Roads, Bradley C. Love

CoRR, 2020